세계 원격 환자 모니터링 인공지능 시장 : 제품, 솔루션, 기술, 지역별 분석(-2030년)

According to Stratistics MRC, the Global Artificial Intelligence In Remote Patient Monitoring Market is accounted for $1.4 billion in 2023 and is expected to reach $7.7 billion by 2030 growing at a CAGR of 27.8% during the forecast period. Remote patient monitoring (RPM), sometimes known as artificial intelligence (AI), is the process of remotely monitoring a patient's health using AI and related technologies. By utilizing a variety of sensors, gadgets, and digital platforms, this technology enables healthcare professionals to track a patient's health state without the need for regular in-person visits. By automating data analysis, offering predictive insights, and enabling more individualized and pro-active healthcare, AI improves RPM. When significant changes or anomalies are found, RPM systems with AI can send alerts and notifications to healthcare providers. Timely intervention is made possible by these notifications.

According to the Centers for Disease Control and Prevention (CDC), more than 18.2 million adults aged 20 and above have coronary artery disease in the U.S.

Market Dynamics:

Driver:

Improved medication adherence

In the context of Remote Patient Monitoring (RPM), artificial intelligence (AI) significantly improves medication adherence. A major problem in healthcare is medication non-adherence, which reduces the efficacy of treatment and raises expenditures. Personalized medication reminders can be sent to patients by AI-powered RPM systems via a variety of media, including mobile apps, text messages, or emails. The patient will find it easier to remember to take their meds as directed, which are customized to the patient's medication schedule. To develop individualized pharmaceutical plans, AI can examine a patient's medical background, present health, and drug routine. These plans ensure that patients receive the best possible treatment recommendations by taking into account elements like dose frequency, pharmaceutical interactions, and potential side effects. Hence all the above factors boost the market growth throughout the extrapolated period.

Restraint:

Data security and privacy

Patient health information is extremely sensitive, and any disclosure of this information may have negative effects. AI in RPM is susceptible to intrusions and data breaches since it relies on gathering and transferring patient data. Patient information may be vulnerable to unauthorized access due to weak encryption techniques or insufficient security measures and the data could potentially be accessed by unauthorized people, putting patients' privacy at risk. RPM's AI algorithms could pick up biases from the training data, which could result in disparate healthcare results for various racial and ethnic groups thus AI systems may worsen healthcare inequities by offering varying degrees of care or diagnostic accuracy for various patient groups if they are not carefully planned and maintained. Thus, all the above factors hamper the growth of the market.

Opportunity:

Cost saving and economical

Remote monitoring driven by AI can spot early warning indications of health decline, enabling prompt interventions. This lessens the need for hospital hospitalizations, especially for the management of chronic diseases and post-operative care. By preventing trips to the emergency department for non-urgent problems, early diagnosis and intervention through remote monitoring can lessen the demand on emergency healthcare services. The long-term cost savings and improved healthcare outcomes make AI in Remote Monitoring an appealing choice for healthcare providers and payers looking to optimize healthcare delivery and cut costs, even though the initial investment in AI technology and infrastructure may be necessary.

Threat:

Low and middle-income countries lack the deployment of artificial intelligence-based remote monitoring solutions

Artificial intelligence (AI) has become a potent tool in the healthcare industry with the potential to revolutionize patient care, cut costs, and enhance outcomes. While AI-based RPM solutions have quickly taken off in high-income nations, their adoption in low- and middle-income nations (LMICs) is still relatively low. The allocation of funding for the purchase and deployment of AI-based RPM systems, which can be expensive, might be difficult in LMICs because healthcare budgets there are frequently tight. Having sufficient hospitals, clinics, and medical professionals with the necessary training can be difficult in some low- and middle-income nations which impedes the market growth.

COVID-19 Impact

The COVID-19 epidemic has pushed the use of gadgets for patient remote monitoring due to the country's government's travel limitations during the pandemic, implementing remote patient monitoring services became urgently necessary. Additionally, healthcare businesses responded quickly to the COVID-19 scenario by providing a huge number of medical gadgets for remote sickness monitoring. For instance, in order to reduce patient interaction and manage health remotely, the U.S. Food and Drug Administration (U.S. FDA) approved Dexcom and Abbott to offer continuous glucose monitoring devices in hospitals in April 2020.

The vital monitors segment is expected to be the largest during the forecast period

The vital monitors segment is estimated to have a lucrative growth, as remote assessment of a patient's health status is made possible by AI-powered vital sign monitors, which are meant to continuously or sporadically collect and evaluate a variety of physiological indicators from patients. When appropriate, these monitors can let healthcare professionals intervene early and with significant insights. To track a patient's heart rate, AI systems might examine electrocardiogram (ECG) data or pulse waveforms. It is possible to monitor both systolic and diastolic blood pressure using cuff-based devices or non-invasive techniques like photoplethysmography (PPG) when there are irregularities in heart rhythm. Hence vital monitor segment contributes to the enhancing growth of the market.

The machine learning segment is expected to have the highest CAGR during the forecast period

The machine learning segment is anticipated to witness the highest CAGR growth during the forecast period, as the effectiveness and efficiency of RPM systems are significantly improved by machine learning (ML), a branch of artificial intelligence. Large amounts of patient data, including vital signs, sensor readings, and electronic medical records, are processed expertly by machine learning algorithms. These algorithms can spot patterns and trends that human caregivers might overlook. For instance, ML can identify small alterations in vital signs that signal a person's health is worsening or a potential medical emergency. Based on past data, ML models can predict the outcomes of patients. These models can forecast disease progression, hospital readmissions, or the likelihood of adverse events by studying patient records and medical histories. This enables healthcare professionals to deliver individualized care plans and intervene pro-actively.

Region with largest share:

Europe is projected to hold the largest market share during the forecast period owing to good legislative conditions, the presence of a sufficient healthcare infrastructure, and the quick uptake of the AI devices, Europe retained the largest share in the market. Additionally, the rollout of these AI assisted monitoring devices in the region is being aided by strategic alliances amongst the businesses to offer patients complete remote patient monitoring, which will increase acceptance. For instance, MTech Mobility and GenieMD signed a partnership agreement in August 2021 to offer their customers a wide range of remote patient monitoring options which are enhancing the market growth in this region.

Region with highest CAGR:

North America is projected to have the highest CAGR over the forecast period, owing to a number of variables, such as an aging population, an increase in chronic diseases, and the demand for affordable healthcare solutions, have contributed to North America's continuous expansion. The COVID-19 epidemic has also sped up the introduction of technologies for remote patient monitoring. A number of businesses in North America are actively working to develop AI-driven applications for remote patient monitoring. These include both well-known healthcare IT firms and emerging AI-focused healthcare businesses. AI-driven RPM solutions and the expansion of telehealth services in North America work in harmony.

Key players in the market

Some of the key players profiled in the Artificial Intelligence In Remote Patient Monitoring Market include: Koninklijke Philips N.V., Medtronic, GE Healthcare, Abbott Laboratories, Resideo Life Care Solutions, Cardiomo Care, Inc., Current Health Limited, Biofourmis Inc., CU-BX Automotive Technologies Ltd., AiCure, LLC, Binah.ai, ChroniSense Medical, Ltd., Huma Therapeutics Limited, Feebris Ltd., iRhythm Technologies, Inc., iHealth Labs, Inc., Gyant.com, Inc., Myia Labs Inc., iBeat, Inc., Neteera Technologies Ltd. and VivaLNK Inc.

Key Developments:

In September 2023, Medtronic Diabetes announces CE Mark for new Simplera™ CGM with disposable all-in-one design. The company's newest no-fingerstick sensor does not require over tape and is seamlessly integrated with the InPen™ smart insulin pen, which provides real-time, personalized dosing guidance

In June 2023, Medtronic presents new data on MiniMed™ 780G system on fixed meal dosing and real-world Time in Range across wide variety of users. hese latest results were presented this weekend at the 83rd American Diabetes Association (ADA) Scientific Sessions in San Diego, CA.

In June 2023, Philips and Masimo introduce new, advanced monitoring capabilities to Philips high acuity patient monitors. The latest extension of Masimo and Philips' ongoing collaboration will help enable clinicians to make quick and informed decisions without the need for additional monitoring equipment.

In May 2023, Philips launches AI-powered CT system to accelerate routine radiology and high-volume screening programs. Powered by AI, the Philips CT 3500 includes a range of image-reconstruction and workflow-enhancing features that help to deliver the consistency, speed, and first-time-right image quality

Products Covered:

• Vital Monitors
• Special Monitors
• Other Products

Solutions Covered:

• Software
• Hardware
• Services

Technologies Covered:

• Natural Language Processing
• Machine Learning
• Querying Method
• Speech Recognition

Applications Covered:

• Diabetes
• Respiratory Issues
• Weight Management & Fitness Monitoring
• Cancer
• Dehydration
• Cardiovascular Diseases
• Sleep Disorder
• Viral Infection
• Other Applications

Regions Covered:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • Italy
  • France
  • Spain
  • Rest of Europe
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • South Korea
  • Rest of Asia Pacific
• South America
  • Argentina
  • Brazil
  • Chile
  • Rest of South America
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • Rest of Middle East & Africa

What our report offers:

• Market share assessments for the regional and country-level segments
• Strategic recommendations for the new entrants
• Covers Market data for the years 2021, 2022, 2023, 2026, and 2030
• Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
• Strategic recommendations in key business segments based on the market estimations
• Competitive landscaping mapping the key common trends
• Company profiling with detailed strategies, financials, and recent developments
• Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

• Company Profiling
  • Comprehensive profiling of additional market players (up to 3)
  • SWOT Analysis of key players (up to 3)
• Regional Segmentation
  • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
  • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

• 2.1 Abstract
• 2.2 Stake Holders
• 2.3 Research Scope
• 2.4 Research Methodology
  • 2.4.1 Data Mining
  • 2.4.2 Data Analysis
  • 2.4.3 Data Validation
  • 2.4.4 Research Approach
• 2.5 Research Sources
  • 2.5.1 Primary Research Sources
  • 2.5.2 Secondary Research Sources
  • 2.5.3 Assumptions

3 Market Trend Analysis

• 3.1 Introduction
• 3.2 Drivers
• 3.3 Restraints
• 3.4 Opportunities
• 3.5 Threats
• 3.6 Product Analysis
• 3.7 Technology Analysis
• 3.8 Application Analysis
• 3.9 Emerging Markets
• 3.10 Impact of Covid-19

4 Porters Five Force Analysis

• 4.1 Bargaining power of suppliers
• 4.2 Bargaining power of buyers
• 4.3 Threat of substitutes
• 4.4 Threat of new entrants
• 4.5 Competitive rivalry

5 Global Artificial Intelligence In Remote Patient Monitoring Market, By Product

• 5.1 Introduction
• 5.2 Vital Monitors
  • 5.2.1 Brain Monitor
  • 5.2.2 Pulse Oximeter
  • 5.2.3 Temperature Monitor
  • 5.2.4 Respiratory Monitor
  • 5.2.5 Blood Pressure Monitor
  • 5.2.6 Heart Rate Monitor
• 5.3 Special Monitors
  • 5.3.1 Blood Glucose Monitor
  • 5.3.2 Multi-Parameter Monitors
  • 5.3.3 Prothrombin Monitors
  • 5.3.4 Cardiac Rhythm Monitor
  • 5.3.5 Fetal Heart Rate Monitor
  • 5.3.6 Anaesthesia Monitors
• 5.4 Other Products

6 Global Artificial Intelligence In Remote Patient Monitoring Market, By Solution

• 6.1 Introduction
• 6.2 Software
• 6.3 Hardware
• 6.4 Services

7 Global Artificial Intelligence In Remote Patient Monitoring Market, By Technology

• 7.1 Introduction
• 7.2 Natural Language Processing
• 7.3 Machine Learning
• 7.4 Querying Method
• 7.5 Speech Recognition

8 Global Artificial Intelligence In Remote Patient Monitoring Market, By Application

• 8.1 Introduction
• 8.2 Diabetes
• 8.3 Respiratory Issues
• 8.4 Weight Management & Fitness Monitoring
• 8.5 Cancer
• 8.6 Dehydration
• 8.7 Cardiovascular Diseases
• 8.8 Sleep Disorder
• 8.9 Viral Infection
• 8.10 Other Applications

11 Global Artificial Intelligence In Remote Patient Monitoring Market, By Geography

• 11.1 Introduction
• 11.2 North America
  • 11.2.1 US
  • 11.2.2 Canada
  • 11.2.3 Mexico
• 11.3 Europe
  • 11.3.1 Germany
  • 11.3.2 UK
  • 11.3.3 Italy
  • 11.3.4 France
  • 11.3.5 Spain
  • 11.3.6 Rest of Europe
• 11.4 Asia Pacific
  • 11.4.1 Japan
  • 11.4.2 China
  • 11.4.3 India
  • 11.4.4 Australia
  • 11.4.5 New Zealand
  • 11.4.6 South Korea
  • 11.4.7 Rest of Asia Pacific
• 11.5 South America
  • 11.5.1 Argentina
  • 11.5.2 Brazil
  • 11.5.3 Chile
  • 11.5.4 Rest of South America
• 11.6 Middle East & Africa
  • 11.6.1 Saudi Arabia
  • 11.6.2 UAE
  • 11.6.3 Qatar
  • 11.6.4 South Africa
  • 11.6.5 Rest of Middle East & Africa

12 Key Developments

• 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 12.2 Acquisitions & Mergers
• 12.3 New Product Launch
• 12.4 Expansions
• 12.5 Other Key Strategies

13 Company Profiling

• 13.1 Koninklijke Philips N.V.
• 13.2 Medtronic
• 13.3 GE Healthcare
• 13.4 Abbott Laboratories
• 13.5 Resideo Life Care Solutions
• 13.6 Cardiomo Care, Inc.
• 13.7 Current Health Limited
• 13.8 Biofourmis Inc.
• 13.9 CU-BX Automotive Technologies Ltd.
• 13.10 AiCure, LLC
• 13.11 Binah.ai
• 13.12 ChroniSense Medical, Ltd.
• 13.13 Huma Therapeutics Limited
• 13.14 Feebris Ltd.
• 13.15 iRhythm Technologies, Inc.
• 13.16 iHealth Labs, Inc.
• 13.17 Gyant.com, Inc.
• 13.18 Myia Labs Inc.
• 13.19 iBeat, Inc.
• 13.20 Neteera Technologies Ltd.
• 13.21 VivaLNK Inc.